ccevolve/baselines/thetaevolve/docker/amd_patch/latest/sglang.patch

diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py
index bdb124e51..3edf30ab1 100644
--- a/python/sglang/srt/configs/model_config.py
+++ b/python/sglang/srt/configs/model_config.py
@@ -454,14 +454,14 @@ class ModelConfig:
             ).lower()
 
             # Detect which checkpoint is it
-            for _, method in QUANTIZATION_METHODS.items():
-                quantization_override = method.override_quantization_method(
-                    quant_cfg, self.quantization
-                )
-                if quantization_override:
-                    quant_method = quantization_override
-                    self.quantization = quantization_override
-                    break
+            # for _, method in QUANTIZATION_METHODS.items():
+            #    quantization_override = method.override_quantization_method(
+            #        quant_cfg, self.quantization
+            #    )
+            #    if quantization_override:
+            #        quant_method = quantization_override
+            #        self.quantization = quantization_override
+            #        break
 
             # Verify quantization configurations.
             if self.quantization is None:
diff --git a/python/sglang/srt/entrypoints/http_server.py b/python/sglang/srt/entrypoints/http_server.py
index 2dd2c75f1..f2adb18f8 100644
--- a/python/sglang/srt/entrypoints/http_server.py
+++ b/python/sglang/srt/entrypoints/http_server.py
@@ -264,6 +264,10 @@ async def validate_json_request(raw_request: Request):
 
 
 @app.get("/health")
+async def health(request: Request) -> Response:
+    return Response(status_code=200)
+
+
 @app.get("/health_generate")
 async def health_generate(request: Request) -> Response:
     """
diff --git a/python/sglang/srt/layers/moe/token_dispatcher/deepep.py b/python/sglang/srt/layers/moe/token_dispatcher/deepep.py
index 372717bf9..40665cc90 100644
--- a/python/sglang/srt/layers/moe/token_dispatcher/deepep.py
+++ b/python/sglang/srt/layers/moe/token_dispatcher/deepep.py
@@ -190,6 +190,7 @@ class DeepEPBuffer:
                     f"Consider using --deepep-config to change the behavior."
                 )
 
+        num_qps_per_rank = 20
         cls._buffer = Buffer(
             group,
             num_nvl_bytes,
diff --git a/python/sglang/srt/layers/quantization/fp8.py b/python/sglang/srt/layers/quantization/fp8.py
index 956264fc9..69f729336 100644
--- a/python/sglang/srt/layers/quantization/fp8.py
+++ b/python/sglang/srt/layers/quantization/fp8.py
@@ -351,10 +351,10 @@ class Fp8LinearMethod(LinearMethodBase):
                 return
             else:
                 weight, weight_scale = layer.weight.data, layer.weight_scale_inv.data
-            layer.weight = torch.nn.Parameter(weight, requires_grad=False)
-            layer.weight_scale_inv = torch.nn.Parameter(
-                weight_scale, requires_grad=False
-            )
+            # layer.weight = torch.nn.Parameter(weight, requires_grad=False)
+            # layer.weight_scale_inv = torch.nn.Parameter(
+            #    weight_scale, requires_grad=False
+            # )
             return
 
         layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 95a529c89..758fbfd5f 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -1359,7 +1359,7 @@ class Scheduler(
 
         if memory_leak:
             msg = "token_to_kv_pool_allocator memory leak detected! " f"{token_msg}"
-            raise ValueError(msg)
+            # raise ValueError(msg)
 
         if self.disaggregation_mode == DisaggregationMode.DECODE:
             req_total_size = (
@@ -1374,7 +1374,7 @@ class Scheduler(
                 f"available_size={len(self.req_to_token_pool.free_slots)}, "
                 f"total_size={self.req_to_token_pool.size}\n"
             )
-            raise ValueError(msg)
+            # raise ValueError(msg)
 
         if (
             self.enable_metrics
@@ -1830,6 +1830,7 @@ class Scheduler(
             deepep_mode=DeepEPMode(self.server_args.deepep_mode),
             require_mlp_tp_gather=require_mlp_tp_gather(self.server_args),
             disable_overlap_schedule=self.server_args.disable_overlap_schedule,
+            offload_tags=self.offload_tags,
         )
 
     def handle_dp_balance_data(self, local_batch: ScheduleBatch):
@@ -1927,6 +1928,7 @@ class Scheduler(
         deepep_mode: DeepEPMode,
         require_mlp_tp_gather: bool,
         disable_overlap_schedule: bool,
+        offload_tags: set[str],
     ):
         # Check if other DP workers have running batches
         if local_batch is None:
@@ -1957,7 +1959,7 @@ class Scheduler(
         )
 
         tbo_preparer = TboDPAttentionPreparer()
-        if disable_overlap_schedule:
+        if len(offload_tags) == 0 and disable_overlap_schedule:
             group = tp_group.device_group
             device = tp_group.device
         else:
diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py
index 58220b1d6..3c3d081a8 100644
--- a/python/sglang/srt/managers/tokenizer_manager.py
+++ b/python/sglang/srt/managers/tokenizer_manager.py
@@ -1044,10 +1044,15 @@ class TokenizerManager:
         request: Optional[fastapi.Request] = None,
     ) -> Tuple[bool, str]:
         self.auto_create_handle_loop()
-        assert (
-            self.server_args.dp_size == 1
-        ), "dp_size must be 1 for init parameter update group"
-        result = (await self.init_weights_update_group_communicator(obj))[0]
+        results = await self.init_weights_update_group_communicator(obj)
+        if self.server_args.dp_size == 1:
+            result = results[0]
+            return result.success, result.message
+        else:
+            all_success = all([r.success for r in results])
+            all_message = [r.message for r in results]
+            all_message = " | ".join(all_message)
+            return all_success, all_message
         return result.success, result.message
 
     async def update_weights_from_distributed(
@@ -1056,9 +1061,6 @@ class TokenizerManager:
         request: Optional[fastapi.Request] = None,
     ) -> Tuple[bool, str]:
         self.auto_create_handle_loop()
-        assert (
-            self.server_args.dp_size == 1 or self.server_args.enable_dp_attention
-        ), "dp_size must be 1 or dp attention must be enabled for update weights from distributed"
 
         if obj.abort_all_requests:
             self.abort_request(abort_all=True)
@@ -1066,8 +1068,15 @@ class TokenizerManager:
         # This means that weight sync
         # cannot run while requests are in progress.
         async with self.model_update_lock.writer_lock:
-            result = (await self.update_weights_from_distributed_communicator(obj))[0]
-            return result.success, result.message
+            results = await self.update_weights_from_distributed_communicator(obj)
+            if self.server_args.dp_size == 1:
+                result = results[0]
+                return result.success, result.message
+            else:
+                all_success = all([r.success for r in results])
+                all_message = [r.message for r in results]
+                all_message = " | ".join(all_message)
+                return all_success, all_message
 
     async def update_weights_from_tensor(
         self,
diff --git a/python/sglang/srt/model_executor/model_runner.py b/python/sglang/srt/model_executor/model_runner.py
index 5222bff0a..ff0bbc62a 100644
--- a/python/sglang/srt/model_executor/model_runner.py
+++ b/python/sglang/srt/model_executor/model_runner.py
@@ -22,6 +22,7 @@ import os
 import time
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
+from contextlib import nullcontext
 
 import torch
 import torch.distributed as dist
@@ -675,7 +676,7 @@ class ModelRunner:
         monkey_patch_vllm_parallel_state()
         monkey_patch_isinstance_for_vllm_base_layer()
 
-        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_WEIGHTS):
+        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_WEIGHTS) if not self.is_draft_worker else nullcontext():
             self.model = get_model(
                 model_config=self.model_config,
                 load_config=self.load_config,
diff --git a/python/sglang/srt/models/glm4_moe.py b/python/sglang/srt/models/glm4_moe.py
index e0f0b373d..a18ac10f1 100644
--- a/python/sglang/srt/models/glm4_moe.py
+++ b/python/sglang/srt/models/glm4_moe.py
@@ -1108,5 +1108,4 @@ class Glm4MoeForCausalLM(DeepseekV2ForCausalLM):
                         )
                         weight_loader(param, loaded_weight)
 
-
 EntryClass = [Glm4MoeForCausalLM]